Material flow & process synchronous simulation in concentrate manufacturing systems.

Cotet, Costel Emil ; Dragoi, George ; Carutasu, George 等

Abstract: In order to optimize manufacturing systems architecture, simulation based material flow management is used to increase productivity by eliminating material flow concentrators. We propose in this paper a new algorithm integrating the process simulation using specialized CAM (Computer Aided Manufacturing) software in the material flow simulation. The result is a synchronous simulation model providing more accurate results. We focused in the case study illustrating our algorithm on a single work point manufacturing system in order to emphasize one of the major difficulties solved by such an algorithm, using stochastic distribution for process as well as for material flow simulation models.

Key words: Discrete material flow simulation, process simulation, concentrate manufacturing systems, productivity.

1. INTRODUCTION

We are using material flow simulation as a tool meant to optimize manufacturing architectures by increasing productivity rates. In this kind of simulation based material flow management some very important parameters must be provided by work point manufacturing processes. During our researches in this area we established two basic different algorithms taking into account manufacturing system characteristics: one for concentrate and one for diffused manufacturing systems. In this paper we will present the concentrate manufacturing systems architecture optimizing algorithm. We define then concentrate manufacturing systems as architectures based on a single work point surrounded & assisted by transport, transfer & deposit facilities.

In order to build a preliminary model of a concentrate manufacturing system architecture data like manufacturing and auxiliary cycle times, mean time between failures, mean time to repair are necessary. In main stream solutions used by research studies as presented in specialized literature very low or none attention is oriented to use special software as a source from where those entry data are provided. In our proposed algorithm the process simulation will be used for every work point related with the material flow simulation.

We consider here a material flow and process synchronous simulation the simulation of a model where at the level at the work point the process simulation is concomitant with the material flow simulation. This allows us to considerably improve the results of the simulation by reducing the difference between simulation reports values and real values obtained during manufacturing.

For a designed manufacturing architecture it is always useful to simulate the material flow conduct before applying our design into practice in order to avoid potential flow concentrators generating low productivity or even blockage (Noel et al., 2003). The leading actor able to manage this area will be the flow simulator.

We agree here with the thesis that within the class of stochastic simulation models, one further distinction is necessary: simulations can be either terminating (sometimes called finite) or nonterminating in nature, with specific algorithms for each category (Sanchez, 2001).

We consider that due to the complexity of the mean time between failure (MTBF) and mean time to repair (MTTR) modeling for various machines and manufacturing systems one can provide the best solutions for such productivity improvement based on stochastic distribution laws and not on fixed values (Cotet & Dragoi, 2003). The algorithm proposed in this paper is based on a set of simulation techniques used to optimize manufacturing systems productivity via optimizing manufacturing systems architecture (Cotet & all, 2005).

2. BUILING A SYNCHRONIOUS MODEL

The main goal of our algorithm was to increase a concentrate manufacturing system productivity by improving the discrete material flow management using the process simulation data at the level of the work point. We had analyzed the results of the preliminary manufacturing architecture of the system material flow simulation and we had identified the flow concentrator based on process simulation results. We had proposed an architecture modification as a solution for eliminating flow concentrators. We had performed a second simulation to validate the optimized manufacturing architecture by obtaining an increased productivity.

In our algorithm some of the necessary data for the material flow simulation like cycle times for the work point defined in our model are provided form CAM simulations describing the manufacturing process (Tichkiewitch et al., 2006). The material flow simulator is integrating the process simulation results at the level at the work point in order to provide a complete model of the manufacturing system (fig. 1).

So in order to realize this integrated simulation model of the manufacturing system using Witness software we started with the process simulation using CATIA NC Manufacturing Solutions.

[FIGURE 1 OMITTED]

That kind of software solution enabled us to define and manage NC programs dedicated to machining parts designed in 3D wire frame or solids geometry using 2.5 to 5-axis machining techniques corresponding with the work points in the Witness model.

An integrated Post Processor engine allows the product to cover the whole manufacturing process from tool trajectory (APT source or Clfile) to NC data. The Machining simulation process model can then be used by the Witness software model for overall manufacturing process integration, simulation and optimization.

3. TRIKS & TRIPS IN SYNCHRONOUS SIMULATION

The concentrate manufacturing system used as a case study to illustrate our algorithm has a work point (machinetool), 2 transport systems (conveyors) 2 buffers and 3 manipulators (figure 1).

One of the main difficulties in synchronizing process and material flow simulation is due to the different approaches in modeling used by the two software solutions: CATIA and Witness. The CAM module of CATIA reproduces every manufacturing cycle identically with the previous. If we simulate the work point activity for 2000 manufacturing cycles the process simulation will be the same every time. In fact the same simulation will be reproduced 2000 times with the same parameters.

On the other side the material flow simulation using Witness software allowed us to change some of the manufacturing cycles introducing stochastic distribution laws and not fixed values for MTBF or MTTR.

In this case some of the 2000 manufacturing cycles will be different due to repair times who will personalize the total 2000 cycles process chain. In order to synchronize the two versions of the 2000 cycles process simulation we had to modify the CAM CATIA program in order to personalize the process simulation. The first step was to introduce a Weibull distribution used by Witness for modeling MTBF in the CATIA cycles chain. In figure 2 the Weibull parameters are [alpha] (the system status), [beta]>0(the system measure) and [gamma]>0 (the system configuration), with real values ([alpha], [beta], [gamma][member of]R). When those modifications are made the simulation time for the process at the work point level and the simulation material flow simulation are synchronous (fig. 3).

4. CONCLUSION

We focus in this paper on several simulation techniques of increasing manufacturing systems performances. In order to evaluate the manufacturing system preliminary architecture we had used a concentrate manufacturing system material flow and a cutting process simulation for a machine-tool work point models based on Witness and CATIA software.

This simulation project was undertaken with the goals of demonstrating and confirming production rates of a manufacturing process based on a proposed design layout and operational data and of identifying ways of improving the design of the system in order to increase those production rates. Our research was focused in applying a set of synchronized simulation techniques used to optimize concentrate manufacturing systems illustrated by a Witness model acting together with the corresponding CATIA CAM model in virtual manufacturing architectures. The main goal was to propose an algorithm able to increase the productivity by improving the discrete material flow management based on the results of reproducing the same stochastic parameters for cutting process simulation cycles as well as for the machine-tool work point in flow simulation. The resulted synchronous simulation model performs the same operations in the same time for the material flow as well as for process simulation.

[FIGURE 2 OMITTED]

[FIGURE 3 OMITTED]

According with this algorithm one can analyze the results of the material flow simulation and identify the flow concentrator for the manufacturing system integrated in the virtual simulation model. If an architecture modification is proposed as a solution for this problem. a second simulation to validate the optimized architecture and the obtained increase of productivity is necessary. Last but not least a financial analysis must confirm the profitability of the manufacturing optimized architecture.

5. REFERENCES

Cotet, C.E., Dragoi, G.S. (2003). Material Flow Management in Validating Concentrate and Diffused FMS Architectures, In: International Journal of Simulation Modelling IJSIMM, no. 4, December 2003, pp.109-120, ISSN 1726-4529, Vienna.

Cotet C.E, Dragoi G., Carutasu N.L. (2005)., Looking for material flow concentrator in diffused manufacturing systems, Annals of DAAAM for 2005 & Proceedings of The 16th INTERNATIONAL DAAAM SYMPOSIUM, "Intelligent Manufacturing & Automation: Focus on Young Researchers and Scientists", Opatija, Croatia, 19-22nd October 2005, pag. 77-78, ISBN 3-901509-46-1, ISSN 1726-9679.

Noel, F.; Brissaud, D. & Tichkiewitch, S., (2003), Integrative Design Environment to Improve Collaboration between Various Experts, CIRP Annals/, STC Dn, Vol 52, No. 1, 2003, pp. 109-116.

Sanchez, S. M. (2001). ABC's of output analysis, Proceedings of The 2001 Winter Simulation 2001, Peters, B.A., Smith, J.S., Medeiros D.J., CD-ROM, Presses Association for Computing Machinery (ACM), New York.

Tichkiewitch, S.; Radulescu, B. & Dragoi, G. (2006). Knowledge management for a cooperative design system, Advances in Design, ElMaraghy & Hoda A. Eds., pp. 97-110, Springer Verlag, ISBN 1-84628-004-4.